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Minimum-Distortion Embedding
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Minimum-Distortion Embedding Paperback - 2021

by Akshay Agrawal; Alnur Ali; Stephen Boyd


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  • Title Minimum-Distortion Embedding
  • Author Akshay Agrawal; Alnur Ali; Stephen Boyd
  • Binding Paperback
  • Pages 188
  • Volumes 1
  • Language ENG
  • Publisher Now Publishers
  • Date 2021-09-08
  • ISBN 9781680838886 / 1680838881
  • Weight 0.6 lbs (0.27 kg)
  • Dimensions 9.21 x 6.14 x 0.4 in (23.39 x 15.60 x 1.02 cm)
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Minimum-Distortion Embedding (Foundations and Trends® in Machine Learning)
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Minimum-Distortion Embedding (Foundations and Trends® in Machine Learning)

by Agrawal, Akshay/ Ali, Alnur/ Boyd, Stephen

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Paperback
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9781680838886 / 1680838881
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Now Publishers Inc, 2021. Paperback. New. 188 pages. 9.21x6.14x0.40 inches.
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Minimum-Distortion Embedding
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Minimum-Distortion Embedding

by Akshay Agrawal

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9781680838886 / 1680838881
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New. Embeddings provide concrete numerical representations of otherwise abstract items, for use in downstream tasks. For example, a biologist might look for subfamilies of related cells by clustering embedding vectors associated with individual cells, while a machine learning practitioner might use vector representations of words as features for a classification task. In this monograph the authors present a general framework for faithful embedding called minimum-distortion embedding (MDE) that generalizes the common cases in which similarities between items are described by weights or distances. The MDE framework is simple but general. It includes a wide variety of specific embedding methods, including spectral embedding, principal component analysis, multidimensional scaling, Euclidean distance problems, etc.The authors provide a detailed description of minimum-distortion embedding problem and describe the theory behind creating solutions to all aspects. They also give describe in detail algorithms… Read More
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Minimum-Distortion Embedding (Foundations and Trends(r) in Machine Learning)
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Minimum-Distortion Embedding (Foundations and Trends(r) in Machine Learning)

by Agrawal, Akshay

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9781680838886 / 1680838881
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paperback. Good. Access codes and supplements are not guaranteed with used items. May be an ex-library book.
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Minimum-Distortion Embedding (Foundations and Trends(r) in Machine Learning)

Minimum-Distortion Embedding (Foundations and Trends(r) in Machine Learning)

by Agrawal, Akshay; Ali, Alnur; Boyd, Stephen

  • New
  • Paperback
Condition
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Binding
Paperback
ISBN 10 / ISBN 13
9781680838886 / 1680838881
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Now Publishers, 2021 8vo (23.5 cm). X, 174 pp. Laminated wrappers. "Embeddings provide concrete numerical representations of otherwise abstract items, for use in downstream tasks. For example, a biologist might look for subfamilies of related cells by clustering embedding vectors associated with individual cells, while a machine learning practitioner might use vector representations of words as features for a classification task. In this monograph the authors present a general framework for faithful embedding called minimum-distortion embedding (MDE) that generalizes the common cases in which similarities between items are described by weights or distances. The MDE framework is simple but general. It includes a wide variety of specific embedding methods, including spectral embedding, principal component analysis, multidimensional scaling, Euclidean distance problems, etc. The authors provide a detailed description of minimum-distortion embedding problem and describe the theory behind creating… Read More
Item Price
SGD 85.12
SGD 22.13 shipping to USA